Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Abed, Mohammed Hamzah; Muhammed, Lamia Abed Noor; Toman, Sarah Hussein

doi:10.48550/arxiv.2008.00148

Cited by 2 publications

(1 citation statement)

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“…Each convolutional layer has its own weights across input, in which each input data entered for the current layer comes from subset of features from the previous layer [20]. Same concept with pooling layer the conduct output of the convolutional layer, it's to minimize the set of features to avoid the complexity cost of features data that moving to the depth [21], [22]. We can see, images feature representations and extracted by each layers is consider as a local features, therefore some fully connected layers introduced in sequence to find the global features which depend on the output of the previous layers  ISSN: 2302-9285 fully-connected layers have a complete connection to all the activities in previous connected as a hierarchical structure, that give the CNN ability to extract more discriminative feature representations from the lower layer to the higher layer.…”

Section: Cnn Architecture Of Proposed Modelmentioning

confidence: 99%

Convolutional neural network for color images classification

2022

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Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.

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Section: Cnn Architecture Of Proposed Modelmentioning

confidence: 99%

Convolutional neural network for color images classification

2022

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An effective and comprehensible method to detect and evaluate retinal damage due to diabetes complications

Dao,

Trinh,

Nguyen

2023

PeerJ Computer Science

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The leading cause of vision loss globally is diabetic retinopathy. Researchers are making great efforts to automatically detect and diagnose correctly diabetic retinopathy. Diabetic retinopathy includes five stages: no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, severe diabetic retinopathy and proliferative diabetic retinopathy. Recent studies have offered several multi-tasking deep learning models to detect and assess the level of diabetic retinopathy. However, the explanation for the assessment of disease severity of these models is limited, and only stops at showing lesions through images. These studies have not explained on what basis the appraisal of disease severity is based. In this article, we present a system for assessing and interpreting the five stages of diabetic retinopathy. The proposed system is built from internal models including a deep learning model that detects lesions and an explanatory model that assesses disease stage. The deep learning model that detects lesions uses the Mask R-CNN deep learning network to specify the location and shape of the lesion and classify the lesion types. This model is a combination of two networks: one used to detect hemorrhagic and exudative lesions, and one used to detect vascular lesions like aneurysm and proliferation. The explanatory model appraises disease severity based on the severity of each type of lesion and the association between types. The severity of the disease will be decided by the model based on the number of lesions, the density and the area of the lesions. The experimental results on real-world datasets show that our proposed method achieves high accuracy of assessing five stages of diabetic retinopathy comparable to existing state-of-the-art methods and is capable of explaining the causes of disease severity.

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Diabetic Retinopathy Diagnosis based on Convolutional Neural Network

Cited by 2 publications

References 0 publications

Convolutional neural network for color images classification

Convolutional neural network for color images classification

An effective and comprehensible method to detect and evaluate retinal damage due to diabetes complications

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